{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import os"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "outputs": [],
   "source": [
    "songs_path='./data/raw/word_level_29'\n",
    "files = os.listdir(songs_path)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "outputs": [
    {
     "data": {
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     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "files"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "outputs": [],
   "source": [
    "seq_filename_list = [] # to keep track of filename from which a sequence is extracted\n",
    "small_file_cntr = 0"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "outputs": [],
   "source": [
    "seqlength=20\n",
    "file=files[1]\n",
    "features = np.load(os.path.join(songs_path, file), allow_pickle=True)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "outputs": [
    {
     "data": {
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     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "features[0][1]"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "outputs": [
    {
     "data": {
      "text/plain": "[['Goodbye', 'Goodbye'],\n ['Norma', 'Norma'],\n ['Jean'],\n ['Though'],\n ['I'],\n ['knew'],\n ['you'],\n ['at'],\n ['all', 'all'],\n ['You'],\n ['had'],\n ['the'],\n ['grace'],\n ['hold'],\n ['yourself', 'yourself'],\n ['While'],\n ['those'],\n ['around', 'around'],\n ['crawled'],\n ['crawled'],\n ['out'],\n ['into', 'into'],\n ['your'],\n ['brain', 'brain'],\n ['They'],\n ['set'],\n ['you'],\n ['on'],\n ['the'],\n ['treadmill', 'treadmill'],\n ['And'],\n ['they'],\n ['made'],\n ['you'],\n ['change'],\n ['your'],\n ['And'],\n ['it'],\n ['seems'],\n ['to'],\n ['me'],\n ['you'],\n ['lived'],\n ['your'],\n ['life', 'life'],\n ['Like'],\n ['a'],\n ['candle', 'candle'],\n ['in'],\n ['the'],\n ['wind'],\n ['Never', 'Never'],\n ['knowing', 'knowing'],\n ['who', 'who'],\n ['to'],\n ['cling'],\n ['to'],\n ['When'],\n ['the'],\n ['rain'],\n ['in'],\n ['And'],\n ['I'],\n ['would'],\n ['have'],\n ['liked'],\n ['to'],\n ['Your'],\n ['ever', 'ever'],\n ['you', 'you'],\n ['the'],\n ['the'],\n ['you'],\n ['the'],\n ['the'],\n ['And'],\n ['you'],\n ['a'],\n ['the'],\n ['you'],\n ['just'],\n ['candle', 'candle'],\n ['Your']]"
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     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "features[0][2]"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "outputs": [
    {
     "data": {
      "text/plain": "[['Good', 'bye'],\n ['Nor', 'ma'],\n ['Jean'],\n ['Though'],\n ['I'],\n ['knew'],\n ['you'],\n ['at'],\n ['al', 'l'],\n ['You'],\n ['had'],\n ['the'],\n ['grace'],\n ['hold'],\n ['your', 'self'],\n ['While'],\n ['those'],\n ['a', 'round'],\n ['crawled'],\n ['crawled'],\n ['out'],\n ['in', 'to'],\n ['your'],\n ['bra', 'in'],\n ['They'],\n ['set'],\n ['you'],\n ['on'],\n ['the'],\n ['tread', 'mill'],\n ['And'],\n ['they'],\n ['made'],\n ['you'],\n ['change'],\n ['your'],\n ['And'],\n ['it'],\n ['seems'],\n ['to'],\n ['me'],\n ['you'],\n ['lived'],\n ['your'],\n ['li', 'fe'],\n ['Like'],\n ['a'],\n ['cand', 'le'],\n ['in'],\n ['the'],\n ['wind'],\n ['Ne', 'ver'],\n ['kno', 'wing'],\n ['w', 'ho'],\n ['to'],\n ['cling'],\n ['to'],\n ['When'],\n ['the'],\n ['rain'],\n ['in'],\n ['And'],\n ['I'],\n ['would'],\n ['have'],\n ['liked'],\n ['to'],\n ['Your'],\n ['e', 'ver'],\n ['yo', 'u'],\n ['the'],\n ['the'],\n ['you'],\n ['the'],\n ['the'],\n ['And'],\n ['you'],\n ['a'],\n ['the'],\n ['you'],\n ['just'],\n ['cand', 'le'],\n ['Your']]"
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "features[0][3]"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[56.0, 0.5, 0.0], [56.0, 1.5, 0.5]]\n",
      "['Good', 'bye']\n",
      "[[56.0, 2.0, 0.0], [54.0, 0.5, 0.0]]\n",
      "['Nor', 'ma']\n",
      "[[56.0, 3.0, 0.0]]\n",
      "['Jean']\n",
      "[[52.0, 1.5, 2.0]]\n",
      "['Though']\n",
      "[[56.0, 1.0, 0.0]]\n",
      "['I']\n",
      "[[61.0, 1.0, 2.0]]\n",
      "['knew']\n",
      "[[61.0, 2.0, 0.0]]\n",
      "['you']\n",
      "[[62.0, 0.5, 0.0]]\n",
      "['at']\n",
      "[[61.0, 1.0, 0.0], [59.0, 2.0, 0.0]]\n",
      "['al', 'l']\n",
      "[[59.0, 1.5, 1.0]]\n",
      "['You']\n",
      "[[61.0, 1.5, 0.0]]\n",
      "['had']\n",
      "[[61.0, 1.5, 0.0]]\n",
      "['the']\n",
      "[[59.0, 2.0, 0.0]]\n",
      "['grace']\n",
      "[[52.0, 0.5, 0.0]]\n",
      "['hold']\n",
      "[[52.0, 1.0, 0.0], [56.0, 1.0, 2.0]]\n",
      "['your', 'self']\n",
      "[[57.0, 2.0, 0.0]]\n",
      "['While']\n",
      "[[56.0, 1.0, 0.0]]\n",
      "['those']\n",
      "[[56.0, 0.5, 2.0], [49.0, 3.0, 0.0]]\n",
      "['a', 'round']\n",
      "[[52.0, 1.0, 8.0]]\n",
      "['crawled']\n",
      "[[52.0, 1.0, 8.0]]\n",
      "['crawled']\n",
      "[[56.0, 0.5, 1.0]]\n",
      "['out']\n",
      "[[61.0, 0.5, 2.0], [61.0, 2.0, 0.0]]\n",
      "['in', 'to']\n",
      "[[62.0, 1.0, 0.0]]\n",
      "['your']\n",
      "[[61.0, 1.0, 0.0], [59.0, 1.0, 2.0]]\n",
      "['bra', 'in']\n",
      "[[61.0, 1.0, 0.0]]\n",
      "['They']\n",
      "[[61.0, 1.5, 0.0]]\n",
      "['set']\n",
      "[[61.0, 0.5, 0.0]]\n",
      "['you']\n",
      "[[59.0, 1.0, 0.0]]\n",
      "['on']\n",
      "[[59.0, 1.5, 0.0]]\n",
      "['the']\n",
      "[[52.0, 2.0, 0.0], [56.0, 1.0, 2.0]]\n",
      "['tread', 'mill']\n",
      "[[56.0, 1.0, 0.0]]\n",
      "['And']\n",
      "[[57.0, 1.0, 0.0]]\n",
      "['they']\n",
      "[[56.0, 1.0, 0.0]]\n",
      "['made']\n",
      "[[54.0, 1.0, 0.0]]\n",
      "['you']\n",
      "[[56.0, 1.0, 0.0]]\n",
      "['change']\n",
      "[[49.0, 3.0, 0.0]]\n",
      "['your']\n",
      "[[56.0, 0.5, 8.0]]\n",
      "['And']\n",
      "[[57.0, 1.5, 0.0]]\n",
      "['it']\n",
      "[[59.0, 1.5, 0.0]]\n",
      "['seems']\n",
      "[[59.0, 1.0, 0.0]]\n",
      "['to']\n",
      "[[59.0, 1.0, 0.0]]\n",
      "['me']\n",
      "[[56.0, 0.5, 2.0]]\n",
      "['you']\n",
      "[[59.0, 1.5, 0.0]]\n",
      "['lived']\n",
      "[[59.0, 1.0, 0.0]]\n",
      "['your']\n",
      "[[59.0, 1.0, 0.0], [56.0, 0.5, 2.0]]\n",
      "['li', 'fe']\n",
      "[[57.0, 1.0, 0.0]]\n",
      "['Like']\n",
      "[[59.0, 1.0, 2.0]]\n",
      "['a']\n",
      "[[59.0, 1.5, 0.0], [61.0, 0.5, 0.0]]\n",
      "['cand', 'le']\n",
      "[[52.0, 2.0, 0.0]]\n",
      "['in']\n",
      "[[56.0, 0.5, 4.0]]\n",
      "['the']\n",
      "[[57.0, 0.5, 0.0]]\n",
      "['wind']\n",
      "[[59.0, 1.0, 0.0], [59.0, 1.0, 0.0]]\n",
      "['Ne', 'ver']\n",
      "[[59.0, 1.0, 0.0], [59.0, 1.5, 0.0]]\n",
      "['kno', 'wing']\n",
      "[[59.0, 1.0, 0.0], [61.0, 1.0, 0.0]]\n",
      "['w', 'ho']\n",
      "[[59.0, 1.0, 1.0]]\n",
      "['to']\n",
      "[[57.0, 1.0, 2.0]]\n",
      "['cling']\n",
      "[[56.0, 1.0, 0.0]]\n",
      "['to']\n",
      "[[56.0, 1.5, 0.0]]\n",
      "['When']\n",
      "[[54.0, 1.5, 0.0]]\n",
      "['the']\n",
      "[[54.0, 1.0, 0.0]]\n",
      "['rain']\n",
      "[[59.0, 1.0, 8.0]]\n",
      "['in']\n",
      "[[59.0, 1.0, 0.0]]\n",
      "['And']\n",
      "[[61.0, 1.0, 0.0]]\n",
      "['I']\n",
      "[[61.0, 1.5, 0.0]]\n",
      "['would']\n",
      "[[61.0, 1.5, 0.0]]\n",
      "['have']\n",
      "[[63.0, 1.0, 0.0]]\n",
      "['liked']\n",
      "[[64.0, 1.0, 0.0]]\n",
      "['to']\n",
      "[[49.0, 0.5, 0.0]]\n",
      "['Your']\n",
      "[[61.0, 2.0, 1.0], [62.0, 1.0, 0.0]]\n",
      "['e', 'ver']\n",
      "[[54.0, 1.0, 0.0], [52.0, 1.0, 0.0]]\n",
      "['yo', 'u']\n",
      "[[59.0, 1.0, 0.0]]\n",
      "['the']\n",
      "[[54.0, 1.0, 0.0]]\n",
      "['the']\n",
      "[[56.0, 1.0, 1.0]]\n",
      "['you']\n",
      "[[52.0, 2.0, 0.0]]\n",
      "['the']\n",
      "[[56.0, 1.5, 0.0]]\n",
      "['the']\n",
      "[[59.0, 0.5, 8.0]]\n",
      "['And']\n",
      "[[63.0, 1.0, 0.0]]\n",
      "['you']\n",
      "[[52.0, 0.75, 2.0]]\n",
      "['a']\n",
      "[[59.0, 0.75, 0.0]]\n",
      "['the']\n",
      "[[56.0, 0.75, 0.0]]\n",
      "['you']\n",
      "[[52.0, 2.5, 0.0]]\n",
      "['just']\n",
      "[[59.0, 1.5, 0.0], [59.0, 1.5, 0.0]]\n",
      "['cand', 'le']\n",
      "[[57.0, 1.5, 0.0]]\n",
      "['Your']\n"
     ]
    }
   ],
   "source": [
    "for midiList, syllList in zip(features[0][1], features[0][3]):\n",
    "    print(midiList)\n",
    "    print(syllList)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "outputs": [],
   "source": [
    "data_matrix=np.zeros((14994, 460))"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "outputs": [],
   "source": [
    "num_syll_features=20\n",
    "num_midi_features=3"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "train_data=np.load('./data/dataset_matrices/train_data_matrix.npy',allow_pickle=True)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "outputs": [],
   "source": [
    "import torch\n",
    "tt=torch.randn((2,3,4))"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "outputs": [],
   "source": [
    "tn=tt.view(tt.shape[1], tt.shape[0], -1)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "outputs": [],
   "source": [
    "d1=np.load('./data/data(11149,20,3).npy',allow_pickle=True)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "outputs": [],
   "source": [
    "t=np.load('./sangle_save/genera_1.npy',allow_pickle=True)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  }
 ],
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